Nonlinearity testing in the case of non Gaussian surrogates, applied to improving analysis of synchronicity in uterine contraction

Analysis of uterine electromyography signals in preterm condition using multifractal algorithm

In this work, an attempt has been made to analyze the preterm (gestation period $\leq 37$ weeks) condition using uterine electromyography (EMG) signals and multifractal detrended fluctuation analysis (MFDFA). The signals recorded from the electrodes placed on the surface of abdomen are used for this study and these are obtained from a publically available online database. These signals are preprocessed using 4-pole digital Butterworth filter. The preprocessed signals are subjected to MFDFA to extract multifractal features namely maximum singularity exponent, peak singularity exponent, strength of multifractality and exponent index. Generalized Hurst exponent extracted from the signals indicate that uterine EMG signals show multifractal behavior in preterm condition. Among the extracted features the coefficient of variation is found to be lower for peak singularity exponent. This indicates that this feature have lower inter-subject variability. Hence, it appears that the multifractal features can help in the assessment of uterine EMG signals for preterm detection.

A multiscale model of the electrohysterogram the BioModUE_PTL project

The electrohysterogram (EHG) is a promising means of monitoring pregnancy and of detecting a risk of preterm labor. To improve our understanding of the EHG as well as its relationship with the physiologic phenomena involved in uterine contractility, we plan to model these phenomena in terms of generation and propagation of uterine electrical activity. This activity can be realistically modeled by representing the principal ionic dynamics at the cell level, the propagation of electrical activity at the tissue level and then the way it is reflected on the skin surface through the intervening tissue. We present in this paper the different steps leading to the development and validation of a biophysics based multiscale model of the EHG, going from the cell to the electrical signal measured on the abdomen.

Better pregnancy monitoring using nonlinear correlation analysis of external uterine electromyography

The objective of this paper is to evaluate the novel method for analyzing the nonlinear correlation of the uterine electromyography (EMG). The application of this method may improve monitoring in pregnancy, labor detection, and preterm labor detection. Uterine EMG signals recorded from a 4 × 4 matrix of electrodes on the subjects' abdomen are used here. The propagation was analyzed using the nonlinear correlation coefficient h(2). Signals from 49 women (36 during pregnancy and 13 in labor) at different gestational age were used. ROC curves were computed to evaluate the potential of three methods to differentiate between 174 contractions recorded during pregnancy and 115 contractions recorded during labor. The results indicate considerably better performance of the nonlinear correlation analysis (area under curve = 0.85) when compared to classical frequency parameters (area under curve = 0.76 and 0.66) in distinguishing labor contractions from normal pregnancy contractions. We conclude that the analysis of the propagation of the uterine electrical activity using the nonlinear correlation coefficient h(2) is a promising way of improving the usefulness of uterine EMG signals for clinical purposes, such as monitoring in pregnancy, labor detection, and prediction of preterm labor.